If you’ve ever Googled “which cloud data warehouse should I use” and ended up more confused than when you started — this post is for you.
Snowflake, Redshift, and BigQuery are the three biggest names in cloud data warehousing right now. They all do similar things. They all use SQL. They all run in the cloud. So how do you choose?
The answer depends on your situation. Let’s break it down.
What They All Have in Common
Before the differences, here’s what all three share:
They store massive amounts of data — we’re talking billions of rows — and let you query it with standard SQL. They all run entirely in the cloud, so there are no servers to buy or maintain. And they’re all used by major companies at scale.
The differences come down to three things: cost model, ecosystem, and who they’re built for.
❄️ Snowflake
The cloud-agnostic powerhouse.
Snowflake is unique because it runs on AWS, Azure, AND Google Cloud. No other major data warehouse does this. That means if your company uses multiple clouds — or might switch clouds in the future — Snowflake gives you flexibility nobody else does.
What makes Snowflake stand out:
Separation of storage and compute. This is Snowflake’s key architectural insight. Storage and compute are completely independent, so you can scale each one separately and only pay for what you actually use.
Multi-cluster warehouses. 100 analysts running queries at the same time? Each gets their own compute cluster. No one slows anyone else down.
Best-in-class data sharing. You can share live data with partners or other teams without copying it. This is genuinely impressive and something the other two struggle to match.
Time Travel. Accidentally deleted a table? You can recover it — up to 90 days back depending on your plan. This has saved countless data teams from disaster.
Zero-Copy Cloning. Clone an entire database instantly with no extra storage cost. Perfect for test environments.
Pricing: Credit-based, per second of compute. Easy to control once you understand it, but can surprise you if you leave warehouses running.
Best for: Teams that work across multiple clouds, organisations that need to share data externally, and companies that want maximum flexibility.
🔴 Redshift
The AWS native. Powerful if you’re already there.
Amazon Redshift is AWS’s data warehouse, and if your company is already deep in the AWS ecosystem, it’s a natural fit. The integration with S3, Lambda, Glue, Kinesis, and the rest of the AWS stack is seamless in a way that Snowflake and BigQuery simply can’t replicate.
What makes Redshift stand out:
Deep AWS integration. If you’re already using S3 for storage, IAM for access control, or Kinesis for streaming — Redshift plugs in with minimal friction.
Redshift Serverless. The newer serverless option means you don’t have to manage clusters anymore. It scales automatically and you pay only for what you use.
Strong performance for large structured datasets. Redshift uses columnar storage and is highly optimised for analytical queries across large volumes of structured data.
Enterprise security. Deep IAM integration, VPC support, encryption at rest and in transit. If your company has strict security requirements, Redshift ticks all the boxes.
Pricing: Traditionally priced per hour for provisioned clusters, which can get expensive at scale. Redshift Serverless is more flexible but costs can still add up. Reserved instances help significantly if you have predictable workloads.
Best for: Companies already deeply invested in AWS who want tight integration with the rest of their AWS infrastructure.
🔵 BigQuery
Google’s beast. Serverless and stupidly fast.
BigQuery is Google Cloud’s data warehouse, and it’s genuinely impressive. It’s the most truly serverless of the three — there’s no infrastructure to think about whatsoever. You just query data and Google handles everything else.
What makes BigQuery stand out:
Truly serverless. No clusters, no warehouses, no infrastructure decisions. Just write SQL and run it.
Generous free tier. 10GB of storage and 1TB of query processing free every month. This makes it the best option for learning or for small projects where you don’t want to spend anything.
BigQuery ML. You can train and run machine learning models using SQL — no Python required. This is genuinely unique and incredibly powerful for teams that want ML without a dedicated data science team.
Looker Studio integration. Connects directly to Google’s BI and visualisation tools with zero setup.
Pricing: Pay per query based on data scanned. This sounds great until someone writes a poorly optimised query on a huge table and gets a surprise bill. Always use LIMIT and partition your tables.
Best for: GCP-native teams, data science and ML workloads, anyone who wants to start for free, and teams already using Google Workspace or Looker.
The Cheat Sheet
| ❄️ Snowflake | 🔴 Redshift | 🔵 BigQuery | |
|---|---|---|---|
| Cloud | Any (AWS/Azure/GCP) | AWS only | GCP only |
| Pricing | Per second | Per hour | Per query |
| Free tier | 30-day trial | 2 months | Always free (1TB/mo) |
| Setup | Easy | Medium | Very easy |
| ML built-in | No | Limited | Yes |
| Data sharing | Best in class | Limited | Good |
Which One Should You Pick?
Pick Snowflake if you work across multiple clouds, your team needs to share data externally, or you want maximum long-term flexibility.
Pick Redshift if your company is already all-in on AWS and you need tight integration with the rest of the AWS ecosystem.
Pick BigQuery if you’re on GCP, you want built-in ML capabilities, or you want to start completely free.
The Honest Take
If you’re just starting out and want to learn, BigQuery is the easiest entry point — free tier, no setup, and you can have your first query running in minutes.
If you’re job hunting in data engineering, Snowflake is the one showing up most in job listings right now. It’s worth learning regardless of which one your company uses.
If you’re at an AWS-heavy company, Redshift is likely already in your stack — and getting good at it will make you immediately useful to your team.
Which one does your company use? Drop a comment below 👇
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